Going beyond linear models, we now investigate \(p\) -GD  in deep-learning settings in its impact on the structure of the learned model and potential implications on the generalization performance.
As we had discussed in Section , the implementation of \(p\) -GD  is straightforward; to illustrate simplicity of implementation, we provide code snippets in
Appendix .
Thus, we are able to effectively experiment with the behaviors \(p\) -GD in neural network training.
Specifically, we perform a set of experiments on the CIFAR-10 dataset [1]}.
We use the stochastic version of \(p\) -GD with different values of \(p\) .
We choose a variety of networks: VGG [2]}, ResNet [3]}, MobileNet [4]} and RegNet [5]}.
